[13th-Jun-2022] News: Published AdaGeo-Lite (article, code)
PyTorch code of AdAGeo: Adaptive-Attentive Geolocalization from few queries: a hybrid approach.
Short presentation (WACV2021): https://www.youtube.com/watch?v=URQCLkDIygM
Requirements:
- Python 3.4+
- Pip3
pip3 install -r *requirements.txt*
Datasets and ResNet18-based AdAGeo are available on request. Datasets details are provided in datasets_details.txt.
Please, set the parameter --allDatasetsPath in const.py before starting. It is the root path (hardcoded) where oxford60k is located.
Train model for a certain target SCENARIO:
- Phase 1: Check AdaGeo-Lite for the quick creation of a pseudo-target dataset.
- Phase 2: Training starting from ResNet18 pretrained on Places365 (code already provides model/weights downloading from project https://github.com/CSAILVision/places365) with default parameters set to the ones declared in our paper.
Using our dataset paths management, you only need to decide the SCENARIO (int value) of Oxford RobotCar dataset and run the command below.
python main.py --expName={what you want} --attention --trainQ=train/queries__queries_biost_few_{SCENARIO} --valQ=val/queries_biost_few_{SCENARIO} --testQ=test/queries_{SCENARIO} --grl --grlDatasets=train/queries+train/queries_biost_few_{SCENARIO}+train/queries_n5_d{SCENARIO} --epochDivider=4 --patience=3
Test model for a certain target SCENARIO:
python eval.py --expName=<what you want> --resume=<path to trained model> --ckpt=best --attention --testQ=test/queries_{SCENARIO}
BibTex:
@ARTICLE{PAOLICELLI-2022-FRONTIERS,
AUTHOR={Paolicelli, Valerio and Berton, Gabriele and Montagna, Francesco and Masone, Carlo and Caputo, Barbara},
TITLE={Adaptive-Attentive Geolocalization From Few Queries: A Hybrid Approach},
JOURNAL={Frontiers in Computer Science},
VOLUME={4},
YEAR={2022},
URL={https://www.frontiersin.org/articles/10.3389/fcomp.2022.841817},
DOI={10.3389/fcomp.2022.841817},
ISSN={2624-9898}
}